Nonparametric inference for the proportionality function in the random censorship model

Nonparametric inference for the proportionality function in the random censorship model

By generalizing the proportional hazards model, we introduce a new function β( t ), which we call the proportionality function, and which we show plays a role in studying aspects of the randomly censored model. We develop an asymptotically efficient nonparametric estimator of β( t ), establish its u...

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Bibliographic Details
Published in:Journal of nonparametric statistics Vol. 15; no. 2; pp. 151 - 169
Main Authors: Hollander, Myles, Laird, Glen, Song, Kai-Sheng
Format: Journal Article
Language:English
Published: Taylor & Francis Group 01-04-2003
Subjects:
Nonparametric Estimation
Proportional Hazards Model
Proportionality Function
Random Censorship Model
Uniform Convergence
Weak Convergence
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Summary:By generalizing the proportional hazards model, we introduce a new function β( t ), which we call the proportionality function, and which we show plays a role in studying aspects of the randomly censored model. We develop an asymptotically efficient nonparametric estimator of β( t ), establish its uniform consistency, and obtain a weak convergence result. Furthermore, a confidence band for β( t ), based on the bootstrap, is developed. The results are applied to an actual dataset.
ISSN:1048-5252
1029-0311
DOI:10.1080/1048525031000089329